1. Field of the Invention
The invention concerns a method to generate a series of magnetic resonance exposures of an examination subject, in which method multiple first measurements are implemented with variation of a measurement parameter that strongly affects a contrast of a first material type excited in the first measurements. Moreover, the invention concerns a magnetic resonance system (also called an “MR system” in the following) with which such a method can be implemented.
2. Description of the Prior Art
In the generation of magnetic resonance exposures, the body to be examined is exposed to a relatively high basic magnetic field of 1.5 Tesla, 3 Tesla or (in newer high magnetic field systems) even of 7 Tesla, for example. A radio-frequency excitation signal is then emitted with a suitable antenna device, which excitation signal causes nuclear spins of specific atoms that are excited to resonance by this radio-frequency field in the basic magnetic field, to be flipped by a specific flip angle relative to the magnetic field lines of the basic magnetic field. The radio-frequency signal radiated upon relaxation of the nuclear spins, known as the magnetic resonance signal, is then detected with suitable antenna devices (which can also be identical to the transmission antenna device). The raw data acquired in such a manner are subsequently used in order to reconstruct the desired image data. For spatial coding, respective defined magnetic field gradients are superimposed on the basic magnetic field during the transmission and the readout or reception of the radio-frequency signals.
A magnetic resonance acquisition has typically included a number of individual partial measurements in which raw data from different slices of the examination subject are acquired in order to subsequently reconstruct volume image data therefrom. In many examinations, it is also necessary to implement multiple magnetic resonance acquisitions—i.e. an entire series of magnetic resonance acquisitions—of the examination subject, wherein a defined measurement parameter is varied from acquisition-to-acquisition. Using the measurements, the effect of this measurement parameter on the examination subject is observed in order to later draw diagnostic conclusions from this. As used herein, a “series” means at least two (but normally more than two) magnetic resonance acquisitions. A measurement parameter is varied so that the contrast of a specific material type excited in the measurements (for example a tissue type of the examination subject or a chemical substance) that is significant for the majority of or specific tissue types (water, for example) is affected as strongly as possible by the variation of the measurement parameter. This ensures that the effect of the measurement parameter on the examination subject is particularly well visible.
A typical example of such a series of magnetic resonance acquisitions is contrast agent examinations, in particular acquisitions known as perfusion measurements. A magnetic resonance-active contrast agent (based on relaxation-promoting gadolinium complexes, for example) is administered to the patient at a specific start point in time, and then the enrichment and washing out of the contrast agent in a defined volume of the examination subject is observed and documented with the use of a series of magnetic resonance acquisitions. The images before, during and/or after administration of the contrast agent can also be compensated with one another. Given perfusion measurements in the brain, up to 100 images or more of the identical volume are acquired continuously at intervals of a few seconds, for example, and the contrast agent distribution is measured. Depending on the temporal and spatial distribution of the contrast agent, regionally different intensity-time curves are observed in the image series, which curves can be converted into perfusion maps with corresponding models.
An additional typical example of a series of magnetic resonance acquisitions with variation of a measurement parameter strongly affecting the contrast is difference imaging methods. In diffusion imaging, multiple images are normally acquired and combined with one another with different diffusion directions and weightings. The strength of the diffusion weighting is mostly defined by what is known as the “b-value”, The diffusion images with different diffusion directions and diffusion weightings or the images combined from these can then be used for diagnostic purposes. Parameter maps with particular diagnostic significance—for example maps that reflect the “Apparent Diffusion Coefficient (ADC)” or the “Fractional Anisotropy (FA)”—can thus be generated by suitable combinations of the acquired diffusion-weighted images.
Since multiple (at least two) successively acquired images are always set in relation to one another (for example a subtraction of the images in the simplest case) in all of these methods, relative deviations in the image geometry can lead to artifacts. Therefore, in MR imaging it is of particularly great importance to be able to register the images to one another correctly, particularly given the evaluation of such series of magnetic resonance exposures. As used herein a “registration” means a spatial association of the image pixels or voxels of two images.
In perfusion measurements or other contrast agent examinations, the spatial association in the successive exposures no longer coincides if the patient moves during the measurement. This can lead to errors in the evaluation. Given acquisition of data from the brain, the precision of the evaluation can in principle be markedly improved by registration of the individual volume data with the assumption of a rigid body movement.
In a diffusion imaging, distortions that depend on the direction and the strength of the diffusion weighting occur due to residual eddy current fields, even without movement of the patient (which can additionally play a role). The precision of the evaluation can in principle be markedly improved via registration of the individual images to one another under the assumption of an affine or complex spatial transformation.
While the registration of images with identical or similar contrast is quite robustly possible with established methods, it is the registration of images with significantly different contrasts that, as before, represents a challenge. Existing methods with which a registration to reduce geometric deviations is conducted given the presence of images of different contrasts typically use a measure of similarity that is optimally independent of the contrast. This measure of similarity is then used within the scope of an optimization method (for example a simplex optimization) in order to determine the parameters of an underlying spatial transformation. A prevalent example of a measure of similarity with such properties is entropy-based “Normalized Mutual Information” (NMI). An explanation of “Normalized Mutual Information” (NMI) is found in [sic] Peter E. Latham and Yasser Roudi (2009), Scholarpedia, 4(1):1658. NMI then precisely delivers a high measure of similarity between two images when an intensity I2 at the same location in a second image is to be simultaneously associated with optimally many pixels or voxels of intensity I1 in the first image. For example, rigid body motion (such as translation and rotation), affine transformations (i.e. transformations with a scaling, a shearing and a displacement or translation) but also transformations of more complex geometry are considered as transformations, for example. Methods for the registration of different images within the scope of diffusion measurements with the use of such optimization methods are described in DE 10 2010 001 577, DE 10 2010 013 605 and US 2010/0171498 A1, for example.
Although with measures of similarity such as NMI it is already possible to compare images of different contrasts with one another and to register them to one another, there are always still cases in which the precision or robustness of the registration is insufficient. In particular, strong variations of the contrast, possibly concomitant with a nearly complete suppression of certain tissue types in one of the images, leads to residual errors given existing methods. However, such strong contrast variations occur given (for example) the administration of contrast agents such as the cited perfusion measurements or in diffusion-weighted imaging.